{"id":"W4320477126","doi":"10.1016/j.engappai.2023.105834","title":"Data-driven failure prediction of Fiber-Reinforced Polymer composite materials","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Composite laminates; Computer science; Fibre-reinforced plastic; Artificial neural network; Composite number; Test data; Fiber; Materials science; Composite material; Structural engineering; Artificial intelligence; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003977062,0.0006528865,0.0002710404,0.0004777247,0.0001521324,0.0002083683,0.0005160357,0.0004933174,0.0005911154],"category_scores_gemma":[0.0009242888,0.0002712599,0.000373307,0.0001892244,0.0002540806,0.0004114825,0.0002301643,0.0005068941,0.0001250593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005898345,"about_ca_system_score_gemma":0.0004377304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006178348,"about_ca_topic_score_gemma":0.006492137,"domain_scores_codex":[0.9999015,0.00001630352,0.000005275216,0.00002680455,0.00003656788,0.000013517],"domain_scores_gemma":[0.9996278,0.0001575319,0.00005808072,0.00002721968,0.0001140652,0.0000153084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004291412,0.0000304005,0.001552535,0.00003038027,0.00001144552,0.0000430182,0.00001211344,0.9756778,0.007433796,0.0002583569,0.00014208,0.01476502],"study_design_scores_gemma":[4.104894e-7,0.000009583463,0.0003731205,8.900604e-7,7.509212e-7,0.000004237414,0.000001078889,0.9979631,0.001521501,0.00009489556,0.00002890984,0.000001513068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6555726,0.0003416573,0.3409105,0.0001109327,0.000041408,0.0000463347,0.0004160181,0.0007721302,0.001788409],"genre_scores_gemma":[0.9853682,0.00005983665,0.01363965,0.00001112104,0.000004306099,0.00002745136,0.0001934218,0.00001471928,0.0006813296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006178348,"threshold_uncertainty_score":0.01228476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882404992180223,"score_gpt":0.3003944498353799,"score_spread":0.2615703999135777,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}